GAN Compression. 
To reduce huge resource consumption during the inference process of GANs, great efforts have emerged in the field of GAN compression in the recent two years.
The search-based methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]} exploited neural architecture search and genetic algorithm to obtain a lightweight generator.
However, they fell into the trap of manual pre-defined search space and huge search costs.
The prune-based methods [7]}, [8]}, [9]}, [10]} directly pruned a lightweight generator architecture from the original generator architecture.
However, these works failed to take discriminator pruning into account, which would seriously destroy the Nash equilibrium between the generator and the discriminator.
The discriminator-free methods [11]}, [3]} directly use the large GAN model as a teacher to distill the lightweight generator without discriminator, which also achieved good performance.
Slimmable GAN [4]} correspondingly shrank the network width of the discriminator with the generator.
However, as shown in Fig. REF , there is no linear between the channel numbers of the optimal discriminator and generator.
Anycost GAN [5]} proposed a generator-conditioned discriminator, which generates the discriminator architecture via passing the generator architecture into the fully connected layer.
However, the fully connected layer is only trained through the original GAN loss, so it is difficult to accurately predict the optimal discriminator architecture for the lightweight generator.
In this work, we design a generator-discriminator cooperative compression (GCC) scheme, which employ a selective activation discriminator and novel online collaborative distillation to boost the performance of the lightweight generator.
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